Mapping-Aware Kernel Partitioning Method for CGRAs Assisted by Deep Learning

Takuya Kojima, Ayaka Ohwada, Hideharu Amano

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Coarse-grained reconfigurable architectures (CGRAs) provide high energy efficiency with word-level programmability rather than bit-level ones such as FPGAs. The coarser reconfigurability brings about higher energy efficiency and reduces the complexity of compiler tasks compared to the FPGAs. However, application mapping process for CGRAs is still time-consuming. When the compiler tries to map a large and complicated application data-flow-graph(DFG) onto the reconfigurable fabric, it tends to result in inefficient resource use or to fail in mapping. In case of failure, the compiler must divide it into several sub-DFGs and goes back to the same flow. In this work, we propose a novel partitioning method based on a genetic algorithm to eliminate the unmappable DFGs and improve the mapping quality. In order not to generate unmappable sub-DFGs, we also propose an estimation model which predicts the mappability and resource requirements using a DGCNN (Deep Graph Convolutional Neural Network). The genetic algorithm with this model can seek the most resource-efficient mapping without the back-end mapping process. Our model can predict the mappability with more than 98% accuracy and resource usage with a negligible error for two studied CGRAs. Besides, the proposed partitioning method demonstrates 53-75% of memory saving, 1.28-1.39x higher throughput, and better mapping quality over three comparative approaches.

Original languageEnglish
Pages (from-to)1213-1230
Number of pages18
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number5
Publication statusPublished - 2022 May 1


  • CGRA
  • Coarse-grained reconfigurable architecture
  • deep learning
  • genetic algorithm
  • graph partitioning

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics


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